Airtable

Deepnote can query data from Airtable bases. Follow these docs to create your own notebook to connect to Airtable, query data, and visualize it.

Connect to Airtable from Deepnote

Create your Airtable token

Follow these instructions to find your token.

Update the token in your notebook

Change the variable AIRTABLE_TOKEN to match your token. If you'd like to keep your data secure, consider storing the token as an environment variable. Environment variables in Deepnote are encrypted and provide a secure way of storing sensitive data.

Find your Airtable base ID

Find the ID of the Airtable base you want to fetch data from by following these instructions.

Update the base ID in your notebook

Change the variable AIRTABLE_BASE_ID to match the base ID you just found.

Update the table name in your notebook

Change the variable AIRTABLE_TABLE_NAME to match the name of the table you want to fetch data from. This is the title of the tab in the Airtable UI.

The name of the table is the name of the tab

Set your keys in the notebook

AIRTABLE_TOKEN = 'your-airtable-token'
AIRTABLE_BASE_ID = 'you-base-id'
AIRTABLE_TABLE_NAME = 'your-table-name'

Use environment variables instead

For more security in your notebook, we strongly recommend to use environment variables

Get your Airtable table from API

Since there was a change in Airtable API, the old way of using pyairtable will not work. But don't worry we got you covered. Use requests to fetch data from API.

We will request the API with our base ID, table name, and token.

url = f'https://api.airtable.com/v0/{AIRTABLE_BASE_ID}/{AIRTABLE_TABLE_NAME}'

headers = {
    'Authorization': f'Bearer {AIRTABLE_TOKEN}',
}
response = requests.get(url, headers=headers)

if response.status_code == 200:
    records = response.json().get('records', [])
    data = [record['fields'] for record in records]
    df = pd.DataFrame(data)
    print(df)

One more function to fetch Airtable table and we are good to go.

def fetch_airtable_data(base_id, table_name, access_token):
    url = f'https://api.airtable.com/v0/{base_id}/{table_name}'
    headers = {'Authorization': f'Bearer {access_token}'}
    all_records = []
    offset = None

    while True:
        params = {'offset': offset} if offset else {}
        response = requests.get(url, headers=headers, params=params)
        if response.status_code != 200:
            print(f"Failed to fetch data: {response.status_code}")
            break

        records = response.json().get('records', [])
        all_records.extend(records)
        offset = response.json().get('offset')

        if not offset:
            break

    data = [record['fields'] for record in all_records]
    return pd.DataFrame(data)

Query Airtable data from Deepnote

The notebook will set up a table object that you can use to fetch any data from your table. For example, the code below fetches the data and converts it to a Pandas dataframe.

df = fetch_airtable_data(AIRTABLE_BASE_ID, AIRTABLE_TABLE_NAME, AIRTABLE_TOKEN)
df

Analyze your data with SQL

You can run queries against our data frame using Deepnote's built-in SQL. You can learn more about SQL blocks on our docs.

SELECT 
  "Estimated Value",
  "Priority",
  "Status"
FROM 
  df
WHERE
  NOT ("Status" = 'Closed' OR "Status" = 'Lost')